A camera's perception of an object typically results from three factors comprising, for example, an orientation of the camera with respect to the object, a depth of a field of the camera associated with the object, and a lens of the camera. By determining these factors associated with a particular image or a video, an accurate image analysis or video analysis can be performed for detection of a target object in the image or the video. Conventional cameras employ methods for sensing the depth associated with a target object. Typically, an image of an object captured by a camera appears different when captured from different perspectives, that is, when the camera is positioned in different orientations with respect to the object. Moreover, lens distortion of the lens of the camera further affects the appearance of the object in the image. Hence, both these factors, that is, camera orientation and camera lens distortion are variables to be considered to perform image analysis.
A conventional digital video camera records video data through an image sensor. An image signal processor processes the video data to enhance the video image quality. The image signal processor then transmits the processed video data to a video data compression processor configured with a video compression technology for compressing the processed video data. The video compression technology depends on different parameters of the video data comprising, for example, type of the video data, size of the video data, etc. A storage unit of the video camera stores the compressed video data in a local disk. The compressed video data can also be transferred to a server or a cloud database for further analytic processing of the video data.
Typically, a conventional camera records an image of an object as seen by the lens of the camera. Consider an example where multiple cameras, for example, a Camera 1, a Camera 2, and a Camera 3 are positioned at different orientations with respect to an object. When Camera 1 is positioned in a straight horizontal line with respect to the object and is oriented to face the object directly, Camera 1 records a complete image of the object without distortion. Therefore, an Image 1a that Camera 1 records, retains a proper aspect ratio between various dimensions of the object. Consider that Camera 2 is positioned on a top left side with respect to the object and is oriented to face diagonally down at the object. The orientation of Camera 2 is different from that of Camera 1 with respect to the target object. If the target object is, for example, a tree, then the aspect ratio of the tree's trunk and the tree's body is reduced in Image 2a that Camera 2 captures, when compared to Image 1a that Camera 1 captured. Therefore, in Image 2a, the tree's body appears slender when compared to the tree's body in Image 1a. Consider that the Camera 3 is positioned on the lower left side with respect to the object and is oriented to face diagonally up at the object. In Image 3a that Camera 3 captures, the tree's trunk appears taller and the tree's body appears larger when viewed from the lens of Camera 3. Thus, the tree's aspect ratio in Image 3a is different from the tree's aspect ratio in Image 1a. This difference in the aspect ratios results in distortions of the recorded images and leads to errors in image analysis. Such errors result in an inaccurate video and image analysis. An inaccurate image and/or video analysis further results in false target object detection due to the distortions in the recorded image and/or video data.
Typically, a video and image analysis system requires a large amount of financial and manpower resources for developing a useful dataset library for an analytic algorithm and an analytic engine. Enriching and generating such dataset libraries is time consuming, tedious, and a continuous process. The dataset library should cover image variations from the perception of a camera, for example, from different orientations of a camera, and should cover environmental factors, for example, climatic changes, lighting changes, etc., that may affect the images of the target object. Moreover, there are different types of cameras being used in the market. Furthermore, the target objects may pose in different forms and shapes, and the recording of such objects may happen at various times and in various seasons. Therefore, developing an analytic dataset library covering different types of cameras and applications is a tedious and time consuming process.
Hence, there is a long felt but unresolved need for a method and an image analysis system that perform an enhanced image analysis for enhanced detection of a target object from an image and for validating the detection of the target object. Moreover, there is a need for a method and an image analysis system that optimize an image analysis by considering the camera orientation and the camera lens distortion variables. Furthermore, there is a need for a method and an image analysis system that configure and enrich an analytic dataset library covering different types of cameras, different orientations of a camera, different environmental factors that may affect the images of the target object, different forms and shapes of the target object, etc.